Systems and methods for processing physiological signals in wavelet space

ABSTRACT

Methods and systems are disclosed for analyzing multiple scale bands in the scalogram of a physiological signal in order to obtain information about a physiological process. An analysis may be performed to identify multiple scale bands that are likely to contain the information sought. Each scale band may be assessed to determine a band quality, and multiple bands may be combined based on the band quality. Information about a physiological process may determined based on the combined band. In an embodiment, analyzing multiple scale bands in a scalogram arising from a wavelet transformation of a photoplethysmograph signal may yield clinically relevant information about, among other things, the blood oxygen saturation of a patient.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application is a continuation of U.S. application Ser. No.12/497,822 (Publication No. US-2011-0004069), filed Jul. 6, 2009, theentire contents of which are incorporated herein by reference.

SUMMARY OF THE DISCLOSURE

The present disclosure relates to physiological signal analysis and,more particularly, the present disclosure relates to analyzing multiplescale bands in a scalogram of a physiological signal in order to obtaininformation about a physiological process.

Different representations of a physiological signal, generated byapplying different transformation techniques, may reveal differentfeatures of the signal. A scalogram representation of a physiologicalsignal may be generated by applying a continuous wavelet transformation,for example, and may allow useful physiological information to bederived. In order to derive such useful information (e.g., clinicalinformation) from a scalogram, an analysis may be performed to identifyone or more scale bands that are likely to contain the informationsought. Often, information about a physiological process of interest iscontained in a first scale band of the scalogram. For example, in thecontext of a plethysmograph signal, information about a patient's bloodoxygen saturation may be contained in a scale band associated with thepatient's pulse rate. However, useful information may also be containedin additional bands. Such additional bands may be related to the firstband, and may arise, for example, because of physiological phenomenasuch as internal reflections. Related bands may provide usefulinformation regarding the physiological process by identifyingphysiologically-relevant features, improving the quality of the derivedinformation, or a combination of the two.

In some applications, related scale bands may be associated with scalesthat are integer multiples of a first scale. For example, a pulse bandin a scalogram may be associated with a scale corresponding to the pulserate, and related bands may be associated with scales corresponding totwice the pulse rate scale, three times the pulse rate scale, etc.

In some applications, related scale bands may be located by identifyingother features within the scalogram, such as ridges or local maxima,which may occur at non-integer multiples of a first scale band.

Certain types of noise and artifact may influence certain scale bandsmore than others. Thus, combining related bands for the purpose ofderiving information about a physiological process may allow for moreconsistent and accurate determination of such information than may beachievable using only a single scale band. A combination may include aweighted combination or a concatenation of data. Combining related bandsmay also include removing a portion or portions of signal that haveundesirable characteristics, such as excess noise.

Because different scale bands may be differently influenced by noise andartifact, and may also reveal different features of the physiologicalprocess of interest, the quality of the information provided by eachscale band may vary between bands and across time. Thus, it may beadvantageous to assess the quality of related scale bands and use theresults of the assessment in combining the bands. For example, a bandwhose quality is determined to be low may have a reduced influence in acombination. A band quality may be assessed in several ways, and may bebased on any one or more of an energy or relative energy of a portion ofthe band, a measure of the consistency of the amplitude of a portion ofthe band, a comparison (e.g., a correlation) of multiple components of areceived signal, and an evaluation in the time domain.

A combination of bands may be used to determine information regarding aphysiological process reflected in the identified bands. For example, acombination of bands related to the pulse band of a plethysmographsignal may be used to determine any of a number of physical parameters,such as blood oxygen saturation. This information may be used in avariety of clinical applications, including within diagnostic andpredictive models, and may be recorded and/or displayed by a patientmonitor.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative patient monitoring system in accordancewith an embodiment;

FIG. 2 is a block diagram of the illustrative patient monitoring systemof FIG. 1 coupled to a patient in accordance with an embodiment;

FIGS. 3( a) and 3(b) show illustrative views of a scalogram derived froma PPG signal in accordance with an embodiment;

FIG. 3( c) shows an illustrative scalogram derived from a signalcontaining two pertinent components in accordance with an embodiment;

FIG. 3( d) shows an illustrative schematic of signals associated with aridge of FIG. 3( c) and illustrative schematics of a further waveletdecomposition of derived signals in accordance with an embodiment;

FIGS. 3( e) and 3(f) are flow charts of illustrative steps involved inperforming an inverse continuous wavelet transform in accordance with anembodiment;

FIG. 4 is a block diagram of an illustrative continuous waveletprocessing system in accordance with an embodiment;

FIGS. 5( a) and 5(b) show illustrative views of scalogramsrepresentative of physiological processes in accordance with anembodiment;

FIG. 6 depicts an illustrative time waveform representative of aphysiological process in accordance with an embodiment; and

FIG. 7 is a flow chart of illustrative steps involved in determiningphysiological information from a physiological signal in accordance withan embodiment.

DETAILED DESCRIPTION

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient) and changes in blood volume in the skin.Ancillary to the blood oxygen saturation measurement, pulse oximetersmay also be used to measure the pulse rate of the patient. Pulseoximeters typically measure and display various blood flowcharacteristics including, but not limited to, the oxygen saturation ofhemoglobin in arterial blood.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may pass light using a lightsource through blood perfused tissue and photoelectrically sense theabsorption of light in the tissue. For example, the oximeter may measurethe intensity of light that is received at the light sensor as afunction of time. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as the photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculatethe amount of the blood constituent (e.g., oxyhemoglobin) being measuredas well as the pulse rate and when each individual pulse occurs.

The light passed through the tissue is selected to be of one or morewavelengths that are absorbed by the blood in an amount representativeof the amount of the blood constituent present in the blood. The amountof light passed through the tissue varies in accordance with thechanging amount of blood constituent in the tissue and the related lightabsorption. Red and infrared (IR) wavelengths may be used because it hasbeen observed that highly oxygenated blood will absorb relatively lessRed light and more IR light than blood with a lower oxygen saturation.By comparing the intensities of two wavelengths at different points inthe pulse cycle, it is possible to estimate the blood oxygen saturationof hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased at least in part on Lambert-Beer's law. The following notationwill be used herein:

I(λ,t)=I ₀(λ)exp(−(sβ ₀(λ)+(1−s)β_(r)(λ))l(t))  (1)

where:λ=wavelength;t=time;I=intensity of light detected;I₀=intensity of light transmitted;s=oxygen saturation;β₀, β_(r)=empirically derived absorption coefficients; andl(t)=a combination of concentration and path length from emitter todetector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., Red and IR), and then calculates saturation by solving for the“ratio of ratios” as follows.

1. The natural logarithm of Eq. 1 is taken (“log” will be used torepresent the natural logarithm) for IR and Red to yield

log I=log I ₀−(sβ ₀+(1−s)β_(r))l.  (2)

2. Eq. 2 is then differentiated with respect to time to yield

$\begin{matrix}{\frac{{\log}\; I}{t} = {\left( {{s\; \beta_{o}} + {\left( {1 - s} \right)\beta_{r}}} \right){\frac{l}{t}.}}} & (3)\end{matrix}$

3. Eq. 3, evaluated at the Red wavelength λ_(R), is divided by Eq. 3evaluated at the IR wavelength λ_(IR) in accordance with

$\begin{matrix}{\frac{{\log}\; {{I\left( \lambda_{R} \right)}/{t}}}{{\log}\; {{I\left( \lambda_{IR} \right)}/{t}}} = {\frac{{s\; {\beta_{o}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}}{{s\; {\beta_{o}\left( \lambda_{IR} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{IR} \right)}}}.}} & (4)\end{matrix}$

4. Solving for s yields

$\begin{matrix}{s = {\frac{{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}{\beta_{r}\left( \lambda_{IR} \right)}}}{\begin{matrix}{{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} -} \\{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}\end{matrix}}.}} & (5)\end{matrix}$

5. Note that, in discrete time, the following approximation can be made:

$\begin{matrix}{\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {{\log \; {I\left( {\lambda,t_{2}} \right)}} - {\log \; {I\left( {\lambda,t_{1}} \right)}}}} & (6)\end{matrix}$

6. Rewriting Eq. 6 by observing that log A−log B=log(A/B) yields

$\begin{matrix}{\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {{\log \left( \frac{I\left( {t_{2},\lambda} \right)}{I\left( {t_{1},\lambda} \right)} \right)}.}} & (7)\end{matrix}$

7. Thus, Eq. 4 can be expressed as

$\begin{matrix}{{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}} \simeq \frac{\log \left( \frac{I\left( {t_{1},\lambda_{R}} \right)}{I\left( {t_{2},\lambda_{R}} \right)} \right)}{\log \left( \frac{I\left( {t_{1},\lambda_{IR}} \right)}{I\left( {t_{2},\lambda_{IR}} \right)} \right)}} = {R.}} & (8)\end{matrix}$

where R represents the “ratio of ratios.”8. Solving Eq. 4 for s using the relationship of Eq. 5 yields

$\begin{matrix}{s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\; {\beta_{r}\left( \lambda_{IR} \right)}}}{{R\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\beta_{o}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}} & (9)\end{matrix}$

9. From Eq. 8, R can be calculated using two points (e.g., PPG maximumand minimum), or a family of points. One method applies a family ofpoints to a modified version of Eq. 8. Using the relationship

$\begin{matrix}{{\frac{{\log}\; I}{t} = \frac{{I}/{t}}{I}},} & (10)\end{matrix}$

Eq. 8 becomes

$\begin{matrix}\begin{matrix}{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}} \simeq \frac{\frac{{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}}{I\left( {t_{1},\lambda_{R}} \right)}}{\frac{{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}}{I\left( {t_{1},\lambda_{IR}} \right)}}} \\{= \frac{\left\lbrack {{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{IR}} \right)}}{\left\lbrack {{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{R}} \right)}}} \\{{= R},}\end{matrix} & (11)\end{matrix}$

which defines a cluster of points whose slope of y versus x will give Rwhen

x=[I(t ₂,λ_(IR))−I(t ₁,λ_(IR))]I(t ₁,λ_(R)),  (12)

and

y=[I(t ₂,λ_(R))−I(t ₁,λ_(R))]I(t ₁,λ_(IR)).  (13)

FIG. 1 is a perspective view of an embodiment of a patient monitoringsystem 10. In an embodiment, system 10 is implemented as part of a pulseoximetry system. System 10 may include a sensor 12 and a monitor 14.Sensor 12 may include an emitter 16 for emitting light at two or morewavelengths into a patient's tissue. A detector 18 may also be providedin sensor 12 for detecting the light originally from emitter 16 thatemanates from the patient's tissue after passing through the tissue.

According to another embodiment and as will be described, system 10 mayinclude a plurality of sensors forming a sensor array in lieu of singlesensor 12. Each of the sensors of the sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of the array may be a charged coupled device (CCD) sensor.In another embodiment, the sensor array may be made up of a combinationof CMOS and CCD sensors. A CCD sensor may comprise a photoactive regionand a transmission region for receiving and transmitting data whereasthe CMOS sensor may be made up of an integrated circuit having an arrayof pixel sensors. Each pixel may have a photodetector and an activeamplifier.

According to an embodiment, emitter 16 and detector 18 may be onopposite sides of a digit such as a finger or toe, in which case thelight that is emanating from the tissue has passed completely throughthe digit. In an embodiment, emitter 16 and detector 18 may be arrangedso that light from emitter 16 penetrates the tissue and is reflected bythe tissue into detector 18, such as a sensor designed to obtain pulseoximetry data from a patient's forehead.

In an embodiment, the sensor or sensor array may be connected to anddraw its power from monitor 14 as shown. In another embodiment, thesensor may be wirelessly connected to monitor 14 and include its ownbattery or similar power supply (not shown). Monitor 14 may beconfigured to calculate physiological parameters based at least in parton data received from sensor 12 relating to light emission anddetection. In an alternative embodiment, the calculations may beperformed on the monitoring device itself and the result of the effortor oximetry reading may be passed to monitor 14. Further, monitor 14 mayinclude a display 20 configured to display a patient's physiologicalparameters or information about the system. In the embodiment shown,monitor 14 may also include a speaker 22 to provide an audible soundthat may be used in various other embodiments, such as sounding anaudible alarm in the event that a patient's physiological parameters arenot within a predefined normal range.

In an embodiment, sensor 12, or the sensor array, may be communicativelycoupled to monitor 14 via a cable 24. However, in other embodiments, awireless transmission device (not shown) or the like may be used insteadof or in addition to cable 24.

In the illustrated embodiment, system 10 may also include amulti-parameter patient monitor 26. The monitor may be cathode ray tubetype, a flat panel display (as shown) such as a liquid crystal display(LCD) or a plasma display, or any other type of monitor now known orlater developed. Multi-parameter patient monitor 26 may be configured tocalculate physiological parameters and to provide a display 28 forinformation from monitor 14 and from other medical monitoring devices orsystems (not shown). For example, multi-parameter patient monitor 26 maybe configured to display an estimate of a patient's blood oxygensaturation (referred to as an “SpO₂” measurement) generated by monitor14, pulse rate information from monitor 14 and blood pressure from ablood pressure monitor (not shown) on display 28.

Monitor 14 may be communicatively coupled to multi-parameter patientmonitor 26 via a cable 32 or 34 that is coupled to a sensor input portor a digital communications port, respectively, and/or may communicatewirelessly (not shown). In addition, monitor 14 and/or multi-parameterpatient monitor 26 may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown). Monitor 14may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

FIG. 2 is a block diagram of a patient monitoring system, such aspatient monitoring system 10 of FIG. 1, which may be coupled to apatient 40 in accordance with an embodiment. Certain illustrativecomponents of sensor 12 and monitor 14 are illustrated in FIG. 2. Sensor12 may include emitter 16, detector 18, and encoder 42. In theembodiment shown, emitter 16 may be configured to emit one or morewavelengths of light (e.g., Red and/or IR) into a patient's tissue 40.Hence, emitter 16 may include a Red light emitting light source such asRed light emitting diode (LED) 44 and/or an IR light emitting lightsource such as IR LED 46 for emitting light into the patient's tissue 40at the wavelengths used to calculate the patient's physiologicalparameters. In one embodiment, the Red wavelength may be between about600 nm and about 700 nm, and the IR wavelength may be between about 800nm and about 1000 nm. In embodiments in which a sensor array is used inplace of a single sensor, each sensor may be configured to emit a singlewavelength. For example, a first sensor may emit only a Red light whilea second may emit only an IR light.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and anysuitable wavelength of electromagnetic radiation may be appropriate foruse with the present techniques. Detector 18 may be chosen to bespecifically sensitive to the chosen targeted energy spectrum of theemitter 16.

In an embodiment, detector 18 may be configured to detect the intensityof light at the Red and IR wavelengths. Alternatively, each sensor inthe array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe Red and IR wavelengths in the patient's tissue 40.

In an embodiment, encoder 42 may contain information about sensor 12,such as what type of sensor it is (e.g., whether the sensor is intendedfor placement on a forehead or digit) and the wavelength or wavelengthsof light emitted by emitter 16. This information may be used by monitor14 to select appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This information mayallow monitor 14 to determine, for example, patient-specific thresholdranges in which the patient's physiological parameter measurementsshould fall and to enable or disable additional physiological parameteralgorithms. Encoder 42 may, for instance, be a coded resistor whichstores values corresponding to the type of sensor 12 or the type of eachsensor in the sensor array, the wavelengths of light emitted by emitter16 on each sensor of the sensor array, and/or the patient'scharacteristics. In another embodiment, encoder 42 may include a memoryon which one or more of the following information may be stored forcommunication to monitor 14: the type of the sensor 12; the wavelengthsof light emitted by emitter 16; the particular wavelength each sensor inthe sensor array is monitoring; a signal threshold for each sensor inthe sensor array; any other suitable information; or any combinationthereof.

In an embodiment, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but are not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to a light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for the Red LED 44and the IR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to RAM54 as QSM 72 fills up. In one embodiment, there may be multiple separateparallel paths having amplifier 66, filter 68, and A/D converter 70 formultiple light wavelengths or spectra received.

In an embodiment, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂ and pulse rate, using variousalgorithms and/or look-up tables based on the value of the receivedsignals and/or data corresponding to the light received by detector 18.Signals corresponding to information about patient 40, and particularlyabout the intensity of light emanating from a patient's tissue overtime, may be transmitted from encoder 42 to a decoder 74. These signalsmay include, for example, encoded information relating to patientcharacteristics. Decoder 74 may translate these signals to enable themicroprocessor to determine the thresholds based on algorithms orlook-up tables stored in ROM 52. User inputs 56 may be used to enterinformation about the patient, such as age, weight, height, diagnosis,medications, treatments, and so forth. Such information may be stored ina suitable memory (e.g., RAM 54) and may allow monitor 14 to determine,for example, patient-specific threshold ranges in which the patient'sphysiological parameter measurements should fall and to enable ordisable additional physiological parameter algorithms. In an embodiment,display 20 may exhibit a list of values which may generally apply to thepatient, such as, for example, age ranges or medication families, whichthe user may select using user inputs 56.

The optical signal through the tissue can be degraded by noise, amongother sources. One source of noise is ambient light that reaches thelight detector. Another source of noise is electromagnetic coupling fromother electronic instruments. Movement of the patient also introducesnoise and affects the signal. For example, the contact between thedetector and the skin, or the emitter and the skin, can be temporarilydisrupted when movement causes either to move away from the skin. Inaddition, because blood is a fluid, it responds differently than thesurrounding tissue to inertial effects, thus resulting in momentarychanges in volume at the point at which a probe or sensor is attached.

Noise (e.g., from patient movement) can degrade a pulse oximetry signalrelied upon by a physician without the physician's awareness. This isespecially true if the monitoring of the patient is remote, the motionis too small to be observed, or the doctor is watching the instrument orother parts of the patient and not the sensor site. Processingphysiological signals may involve operations that reduce the amount ofnoise present in the signals or otherwise identify noise components inorder to prevent them from affecting measurements of physiologicalparameters derived from the physiological signals.

It will be understood that the present disclosure is applicable to anysuitable signals and that PPG signals may be used merely forillustrative purposes. Those skilled in the art will recognize that thepresent disclosure has wide applicability to other signals including,but not limited to other biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

In one embodiment, a physiological signal may be transformed using acontinuous wavelet transform. Information derived from the transform ofthe physiological signal (i.e., in wavelet space) may be used to providemeasurements of one or more physiological parameters.

The continuous wavelet transform of a signal x(t) in accordance with thepresent disclosure may be defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}\ {t}}}}} & (14)\end{matrix}$

where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by Eq. 14 may be used to construct arepresentation of a signal on a transform surface. The transform may beregarded as a time-scale representation. Wavelets are composed of arange of frequencies, one of which may be denoted as the characteristicfrequency of the wavelet, where the characteristic frequency associatedwith the wavelet is inversely proportional to the scale a. One exampleof a characteristic frequency is the dominant frequency. Each scale of aparticular wavelet may have a different characteristic frequency. Theunderlying mathematical detail required for the implementation within atime-scale can be found, for example, in Paul S. Addison, TheIllustrated Wavelet Transform Handbook (Taylor & Francis Group 2002),which is hereby incorporated by reference herein in its entirety.

The continuous wavelet transform decomposes a signal using wavelets,which are generally highly localized in time. The continuous wavelettransform may provide a higher resolution relative to discretetransforms, thus providing the ability to garner more information fromsignals than typical frequency transforms such as Fourier transforms (orany other spectral techniques) or discrete wavelet transforms.Continuous wavelet transforms allow for the use of a range of waveletswith scales spanning the scales of interest of a signal such that smallscale signal components correlate well with the smaller scale waveletsand thus manifest at high energies at smaller scales in the transform.Likewise, large scale signal components correlate well with the largerscale wavelets and thus manifest at high energies at larger scales inthe transform. Thus, components at different scales may be separated andextracted in the wavelet transform domain. Moreover, the use of acontinuous range of wavelets in scale and time position allows for ahigher resolution transform than is possible relative to discretetechniques.

In addition, transforms and operations that convert a signal or anyother type of data into a spectral (i.e., frequency) domain necessarilycreate a series of frequency transform values in a two-dimensionalcoordinate system where the two dimensions may be frequency and, forexample, amplitude. For example, any type of Fourier transform wouldgenerate such a two-dimensional spectrum. In contrast, wavelettransforms, such as continuous wavelet transforms, are required to bedefined in a three-dimensional coordinate system and generate a surfacewith dimensions of time, scale and, for example, amplitude. Hence,operations performed in a spectral domain cannot be performed in thewavelet domain; instead the wavelet surface must be transformed into aspectrum (i.e., by performing an inverse wavelet transform to convertthe wavelet surface into the time domain and then performing a spectraltransform from the time domain). Conversely, operations performed in thewavelet domain cannot be performed in the spectral domain; instead aspectrum must first be transformed into a wavelet surface (i.e., byperforming an inverse spectral transform to convert the spectral domaininto the time domain and then performing a wavelet transform from thetime domain). Nor does a cross-section of the three-dimensional waveletsurface along, for example, a particular point in time equate to afrequency spectrum upon which spectral-based techniques may be used. Atleast because wavelet space includes a time dimension, spectraltechniques and wavelet techniques are not interchangeable. It will beunderstood that converting a system that relies on spectral domainprocessing to one that relies on wavelet space processing would requiresignificant and fundamental modifications to the system in order toaccommodate the wavelet space processing (e.g., to derive arepresentative energy value for a signal or part of a signal requiresintegrating twice, across time and scale, in the wavelet domain while,conversely, one integration across frequency is required to derive arepresentative energy value from a spectral domain). As a furtherexample, to reconstruct a temporal signal requires integrating twice,across time and scale, in the wavelet domain while, conversely, oneintegration across frequency is required to derive a temporal signalfrom a spectral domain. It is well known in the art that, in addition toor as an alternative to amplitude, parameters such as energy density,modulus, phase, among others, may all be generated using such transformsand that these parameters have distinctly different contexts andmeanings when defined in a two-dimensional frequency coordinate systemrather than a three-dimensional wavelet coordinate system. For example,the phase of a Fourier system is calculated with respect to a singleorigin for all frequencies while the phase for a wavelet system isunfolded into two dimensions with respect to a wavelet's location (oftenin time) and scale.

The energy density function of the wavelet transform, the scalogram, isdefined as

S(a,b)=|T(a,b)|²  (15)

where ‘∥’ is the modulus operator. The scalogram may be rescaled foruseful purposes. One common rescaling is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{{{T\left( {a,b} \right)}}^{2}}{a}} & (16)\end{matrix}$

and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as a locus of points oflocal maxima in the plane. A ridge associated with only the locus ofpoints of local maxima in the plane is labeled a “maxima ridge.” Alsoincluded as a definition of a ridge are paths displaced from the locusof the local maxima. Any reasonable definition of a ridge may beemployed in the methods described herein.

For implementations requiring fast numerical computation, the wavelettransform may be expressed as an approximation using Fourier transforms.Pursuant to the convolution theorem, because the wavelet transform isthe cross-correlation of the signal with the wavelet function, thewavelet transform may be approximated in terms of an inverse FFT of theproduct of the Fourier transform of the signal and the Fourier transformof the wavelet for each required a scale and a multiplication of theresult by √{square root over (a)}. In the discussion of the technologywhich follows herein, the term “scalogram” may be taken to include allsuitable forms of rescaling including, but not limited to, the originalunscaled wavelet representation, linear rescaling, any power of themodulus of the wavelet transform, or any other suitable rescaling. Inaddition, for purposes of clarity and conciseness, the term “scalogram”shall be taken to mean the wavelet transform T(a,b) itself, or any partthereof. For example, the real part of the wavelet transform, theimaginary part of the wavelet transform, the phase of the wavelettransform, any other suitable part of the wavelet transform, or anycombination thereof is intended to be conveyed by the term “scalogram.”

A scale, which may be interpreted as a representative temporal period,may be converted to a characteristic frequency of the wavelet function.The characteristic frequency associated with a wavelet of arbitrary ascale is given by

$\begin{matrix}{{f = \frac{f_{c}}{a}},} & (17)\end{matrix}$

where f_(c) is the characteristic frequency of the mother wavelet (i.e.,at a=1) and becomes a scaling constant, and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in connection with the presentdisclosure. One of the most commonly used complex wavelets, the Morletwavelet, is defined as

ψ(t)=π^(−1/4)(e ^(i2πf) ⁰ ^(t) e ^(−(2πf) ⁰ ⁾ ² ^(/2))e-^(−t) ²^(/2),  (18)

where f₀ is the central frequency of the mother wavelet. The second termin the parentheses is known as the correction term, as it corrects forthe non-zero mean of the complex sinusoid within the Gaussian window. Inpractice, it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi (t)} = {\frac{1}{\pi^{1/4}}^{\; 2\pi \; f_{0}t}{^{{- t^{2}}/2}.}}} & (19)\end{matrix}$

This wavelet is a complex wave within a scaled Gaussian envelope. Whileboth definitions of the Morlet wavelet are included herein, the functionof Eq. 19 is not strictly a wavelet as it has a non-zero mean (i.e., thezero frequency term of its corresponding energy spectrum is non-zero).However, it will be recognized by those skilled in the art that Eq. 19may be used in practice with f₀>>0 with minimal error and is included(as well as other similar near wavelet functions) in the definition of awavelet herein. A more detailed overview of the underlying wavelettheory, including the definition of a wavelet function, can be found inthe general literature. Discussed herein is how wavelet transformfeatures may be extracted from the wavelet decomposition of signals. Forexample, wavelet decomposition of PPG signals may be used to provideclinically useful information.

Pertinent repeating features in a signal give rise to a time-scale bandin wavelet space or a rescaled wavelet space. For example, the pulsecomponent of a PPG signal produces a dominant band in wavelet space ator around the pulse frequency. FIGS. 3( a) and (b) show two views of anillustrative scalogram derived from a PPG signal, according to anembodiment. The figures show an example of the band caused by the pulsecomponent in such a signal. The pulse band is located between the dashedlines in the plot of FIG. 3( a). The band is formed from a series ofdominant coalescing features across the scalogram. This can be clearlyseen as a raised band across the transform surface in FIG. 3( b) locatedwithin the region of scales indicated by the arrow in the plot(corresponding to 60 beats per minute). The maxima of this band withrespect to scale is the ridge. The locus of the ridge is shown as ablack curve on top of the band in FIG. 3( b). By employing a suitablerescaling of the scalogram, such as that given in Eq. 16, the ridgesfound in wavelet space may be related to the instantaneous frequency ofthe signal. In this way, the pulse rate may be obtained from the PPGsignal. Instead of rescaling the scalogram, a suitable predefinedrelationship between the scale obtained from the ridge on the waveletsurface and the actual pulse rate may also be used to determine thepulse rate.

By mapping the time-scale coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way, both times betweenindividual pulses and the timing of components within each pulse may bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, or perform any other suitable calculations ordiagnostics. Alternative definitions of a ridge may be employed.Alternative relationships between the ridge and the pulse frequency ofoccurrence may be employed.

As discussed above, pertinent repeating features in the signal give riseto a time-scale band in wavelet space or a rescaled wavelet space. For aperiodic signal, this band remains at a constant scale in the time-scaleplane. For many real signals, especially biological signals, the bandmay be non-stationary, and may vary in scale, amplitude, or both, overtime. FIG. 3( c) shows an illustrative schematic of a wavelet transformof a signal containing two pertinent components leading to two bands inthe transform space, according to an embodiment. These bands are labeledband A and band B on the three-dimensional schematic of the waveletsurface. In an embodiment, a band ridge is defined as the locus of thepeak values of these bands with respect to scale. For purposes ofdiscussion, it may be assumed that band B contains the signalinformation of interest. Band B will be referred to as the “primaryband.” In addition, it may be assumed that the system from which thesignal originates, and from which the transform is subsequently derived,exhibits some form of coupling between the signal components in band Aand band B. When noise or other erroneous features are present in thesignal with similar spectral characteristics of the features of band B,then the information within band B can become ambiguous (i.e., obscured,fragmented or missing). In this case, the ridge of band A (referred toherein as “ridge A”) may be followed in wavelet space and extractedeither as an amplitude signal or a scale signal which will be referredto as the “ridge amplitude perturbation” (RAP) signal and the “ridgescale perturbation” (RSP) signal, respectively. The RAP and RSP signalsmay be extracted by projecting the ridge onto the time-amplitude ortime-scale planes, respectively. The top plots of FIG. 3( d) show aschematic of the RAP and RSP signals associated with ridge A in FIG. 3(c). Below these RAP and RSP signals are schematics of a further waveletdecomposition of these newly derived signals. This secondary waveletdecomposition allows for information in the region of band B in FIG. 3(c) to be made available as band C and band D. The ridges of bands C andD may serve as instantaneous time-scale characteristic measures of thesignal components causing bands C and D. This technique, which will bereferred to herein as secondary wavelet feature decoupling (SWFD), mayallow information concerning the nature of the signal componentsassociated with the underlying physical process causing the primary bandB (FIG. 3( c)) to be extracted when band B itself is obscured in thepresence of noise or other erroneous signal features.

In some instances, an inverse continuous wavelet transform may bedesired, such as when modifications to a scalogram (or modifications tothe coefficients of a transformed signal) have been made in order to,for example, remove artifacts, remove noise, combine bands, or anycombination thereof. In one embodiment, there is an inverse continuouswavelet transform which allows the original signal to be recovered fromits wavelet transform by integrating over all scales and locations, aand b, in accordance with

$\begin{matrix}{{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi\left( \frac{t - b}{a} \right)}\ \frac{{a}\ {b}}{a^{2}}}}}}},} & (20)\end{matrix}$

which may also be written as

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}{\psi_{a,b}(t)}\ {\frac{{a}\ {b}}{a^{2}}.}}}}}} & (21)\end{matrix}$

where C_(g) is a scalar value known as the admissibility constant. It iswavelet-type dependent and may be calculated in accordance with

$\begin{matrix}{C_{g} = {\int_{0}^{\infty}{\frac{{{\hat{\psi}(f)}}^{2}}{f}\ {{f}.}}}} & (22)\end{matrix}$

FIG. 3( e) is a flow chart of illustrative steps that may be taken toperform an inverse continuous wavelet transform in accordance with theabove discussion. An approximation to the inverse transform may be madeby considering Eq. 20 to be a series of convolutions across scales. Itshall be understood that there is no complex conjugate here, unlike forthe cross correlations of the forward transform. As well as integratingover all of a and b for each time t, this equation may also takeadvantage of the convolution theorem which allows the inverse wavelettransform to be executed using a series of multiplications. FIG. 3( f)is a flow chart of illustrative steps that may be taken to perform anapproximation of an inverse continuous wavelet transform. It will beunderstood that any other suitable technique for performing an inversecontinuous wavelet transform may be used in accordance with the presentdisclosure.

The present disclosure relates to methods and systems for processing asignal using the above mentioned techniques in analyzing multiple scalebands in the scalogram of a signal in order to obtain information abouta process represented by the signal. An analysis may be performed toidentify multiple scale bands that are likely to contain the informationsought. Each scale band may be assessed to determine a band quality, andmultiple bands may be combined based on the band quality. Informationabout the process may determined based on the combined band.

It will be understood that the present disclosure is applicable to anysuitable signals and that physiological signals may be used merely forillustrative purposes. Those skilled in the art will recognize that thepresent disclosure has wide applicability to other signals including,but not limited to other biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

The methods for determining physiological information from a signal inwavelet space described in this disclosure may be implemented on amultitude of different systems and apparatuses through the use ofhuman-readable or machine-readable information. For example, the methodsdescribed herein may be implemented using machine-readable computer codeand executed on a computer system that is capable of reading thecomputer code. An exemplary system that is capable of wavelet signalanalysis is depicted in FIG. 4.

FIG. 4 is a block diagram of an illustrative wavelet processing systemin accordance with an embodiment. In an embodiment, input signalgenerator 410 generates an input signal 416. As illustrated, inputsignal generator 410 may include oximeter 420 coupled to sensor 418,which may provide as input signal 416, a PPG signal. It will beunderstood that input signal generator 410 may include any suitablesignal source, signal generating data, signal generating equipment, orany combination thereof, to produce signal 416. Signal 416 may be anysuitable signal or signals, such as, for example, biosignals (e.g.,electrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, ultrasound, orany other suitable biosignal), dynamic signals, non-destructive testingsignals, condition monitoring signals, fluid signals, geophysicalsignals, astronomical signals, electrical signals, financial signalsincluding financial indices, sound and speech signals, chemical signals,meteorological signals including climate signals, and/or any othersuitable signal, and/or any combination thereof.

In an embodiment, signal 416 may be coupled to processor 412. Processor412 may be any suitable software, firmware, hardware, and/orcombinations thereof, for processing signal 416. For example, processor412 may include one or more hardware processors (e.g., integratedcircuits), one or more software modules, computer-readable media such asmemory, firmware, or any combination thereof. Processor 412 may, forexample, be a computer or may be one or more chips (i.e., integratedcircuits). Processor 412 may perform the calculations associated withthe transforms of the present disclosure as well as the calculationsassociated with any suitable interrogations of the transforms. Processor412 may perform any suitable signal processing of signal 416 to filtersignal 416, such as any suitable band-pass filtering, adaptivefiltering, closed-loop filtering, any other suitable filtering, and/orany combination thereof.

Processor 412 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. The memory may be used by processor 412to, for example, store data corresponding to a continuous wavelettransform of input signal 416, such as data representing a scalogram. Inone embodiment, data representing a scalogram may be stored in RAM ormemory internal to processor 412 as any suitable three-dimensional datastructure such as a three-dimensional array that represents thescalogram as energy levels in a time-scale plane. Any other suitabledata structure may be used to store data representing a scalogram. Thememory may be used by processor 412, to, for example, store any datarelated to any of the calculations described herein, includingidentifying scale bands, assessing band quality, combining scale bands,and determining physiological information, among others. This storagemay take the form of any suitable data structure.

Processor 412 may be coupled to output 414. Output 414 may be anysuitable output device such as one or more medical devices (e.g., amedical monitor that displays various physiological parameters, amedical alarm, or any other suitable medical device that either displaysphysiological parameters or uses the output of processor 412 as aninput), one or more display devices (e.g., monitor, PDA, mobile phone,any other suitable display device, or any combination thereof), one ormore audio devices, one or more memory devices (e.g., hard disk drive,flash memory, RAM, optical disk, any other suitable memory device, orany combination thereof), one or more printing devices, any othersuitable output device, or any combination thereof.

It will be understood that system 400 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal generator 410 may beimplemented as parts of sensor 12 and monitor 14 and processor 412 maybe implemented as part of monitor 14.

The present disclosure provides techniques for determining informationfrom representative signals by analyzing the signals in the waveletdomain, the time domain, and combinations of the two. In someembodiments, a signal may be analyzed in both domains in sequentialsteps, which may be performed in any suitable order. Examples of waveletdomain representations of signals are depicted in FIGS. 5( a) and 5(b),while time domain representations of signals are depicted in FIG. 6.These examples are discussed below and may illustrate the systems andmethods that follow.

FIGS. 5( a) and 5(b) depict illustrative scalograms 502 and 504 of PPGsignals that may be analyzed in accordance with an embodiment.Scalograms 502 and 504 may be generated and analyzed within system 10 ofFIGS. 1 and 2 or system 400 of FIG. 4 as described above.

The PPG signal represented by scalogram 502 of FIG. 5( a) was measuredfrom a patient in a relatively noise-free environment. Noise was thenadded to this PPG signal to produce the signal represented by scalogram504 of FIG. 5( b). These two scalograms may be compared to illustrateseveral features, including several ways in which noise may introducevariation between scale bands within a wavelet domain representation ofa signal.

Scalogram 502 may include first band 506 and related bands 508, 510 and512. Related bands 508-512 may be located at scales that areapproximately integer multiples of a scale associated with first band506. In an embodiment, first band 506 may be a pulse band of a PPGsignal and may be associated with a scale corresponding to a pulse rate.Scalogram 504 may include first band 514 and related bands 516, 518 and520. Related bands 516-520 may be located at scales that areapproximately integer multiples of a scale associated with first band514. In an embodiment, first band 514 may be a pulse band of a PPGsignal, and may correspond to first band 506 of scalogram 502. Bands506-512 of scalogram 502 and bands 514-520 of scalogram 504 may beidentified and analyzed for features that may communicate usefulinformation about a physiological process reflected in the underlyingPPG signals.

For example, scale bands 506-512 of scalogram 502 may be typical ofscale bands that communicate information regarding a physiologicalprocess in a healthy patient under low-noise conditions. These scalebands 506-512 may be compared to corresponding scale bands 514-520 ofscalogram 504. The PPG signal represented by scalogram 504 may havearisen, for example, from a patient experiencing low blood oxygenperfusion, resulting in a PPG signal that is obscured by other factors(e.g., hardware noise). One feature of scale bands 514-520 that suggestssuch an interpretation, for example, is the non-uniformity of thescalogram across each scale band and between scale bands. Techniques forassessing the quality of the scale bands with respect to informationabout an underlying physiological process are discussed in detail below.

FIG. 6 depicts illustrative time waveforms 602 and 604 derived from aPPG signal that may be analyzed in accordance with an embodiment. Timewaveforms that may be analyzed by the methods described herein may bebased directly on received signals such as input signal 416, or may bebased on time waveforms arising from an inverse transformation of asignal in another domain. For example, a time domain signal may begenerated by applying an inverse continuous wavelet transformation to ascalogram or a modified scalogram, as discussed above. Multiple timewaveforms may arise from multiple components of a received signal (e.g.,via input signal 416) and may be compared. For example, a receivedsignal may include time waveform 602 based on a Red PPG signal and timewaveform 604 based on an IR PPG signal

An analysis of time waveforms 602 and 604 may be performed to determineinformation about a physiological process. In an embodiment, an analysismay identify one or more subwindows within at least one of timewaveforms 602 and 604 that may be less suitable for use in determiningphysiological information. For example, an analysis may determine thatthe time waveforms 602 and 604 within the subwindows indicated byhighlight region 606 are not sufficiently correlated. A patientmonitoring system, such as system 10, may selectively discard or reducethe contribution of the time waveforms 602 and 604 within the subwindowsindicated by highlight region 606. In an embodiment, time-domain signalprocessing techniques may be applied in conjunction with wavelet-domainsignal processing techniques to determine information from aphysiological signal. Examples of such embodiments, among others, willbe discussed in detail below with reference to process 700 of FIG. 7.

FIG. 7 is a flow chart of illustrative steps in a process 700 involvedin determining physiological information in accordance with anembodiment. Process 700 may be performed by processor 412, or may beperformed by any suitable processing device communicatively coupled tomonitor 14. Process 700 may be performed by a digital processing device,or implemented in analog hardware.

Process 700 may be executed over a sliding window of a physiologicalsignal. For example, process 700 may analyze the previous N samples ofthe physiological signal, or the samples of the physiological signalreceived in the previous T units of time. The length of the slidingwindow over which process 700 is executed may be fixed or dynamic. In anembodiment, the length of the sliding window may be based at least inpart on the noise content of a physiological signal. For example, thelength of the sliding window may increase with increasing noise. In anembodiment, the length of the sliding window over which process 700 isexecuted may be based at least in part on a patient condition. Forexample, the length of the sliding window may decrease when a patient isundergoing more rapid changes in physiological state.

It will be noted that the steps of process 700 may be performed in anysuitable order, and certain steps may be omitted entirely, as will bediscussed in additional detail below.

At step 702, a signal may be received. The signal (e.g., a PPG signal)may be received from any suitable source (e.g., patient 40) using anysuitable technique. A received signal may be generated by sensor unit12, which may itself include any of the number of physiological sensorsdescribed herein. The received signal may be signal 416, which may begenerated by an oximeter 420 coupled between processor 412 and sensor418. The received signal may include multiple signals, for example, inthe form of a multi-dimensional vector signal or a frequency- ortime-multiplexed signal. Additionally, the signal received at step 702may be a derived signal generated internally to processor 412.Accordingly, the received signal may be a transformation of a signal416, or may be a transformation of multiple such signals. For example,the received signal may be a ratio of two signals. The received signalmay be based at least in part on past values of a signal, such as signal416, which may be retrieved by processor 412 from a memory such as abuffer memory or RAM 54.

In an embodiment, the signal received in step 702 may be a PPG signalwhich may be obtained from sensor 12 that may be coupled to patient 40.The PPG signal may be obtained from input signal generator 410, whichmay include oximeter 420 coupled to sensor 418, which may provide a PPGsignal as signal 416. In an embodiment, the PPG signal may be obtainedfrom patient 40 using sensor 12 or input signal generator 410 in realtime. In an embodiment, the PPG signal may have been stored in ROM 52,RAM 52, and/or QSM 72 (FIG. 2) in the past and may be accessed bymicroprocessor 48 within monitor 14 to be processed. Signal 416 mayinclude one or more of a Red PPG signal component and an IR PPG signalcomponent. Process 700 may be performed on an IR PPG signal component ofa received signal, a Red PPG signal component of a received signal, or acombination thereof.

At step 704, the signal received at step 702 may be transformed. In anembodiment, processor 412 may transform the signal into any suitabledomain, for example, a Fourier, wavelet, spectral, scale, time,time-spectral, time-scale domains, or any transform space. Thistransformation may be performed by any one or more of the transformationtechniques described herein, including a continuous wavelettransformation. This transformation may be performed by any suitableprocessing device, such as processor 412, which may itself be ageneral-purpose computing device or a specialized processor. Thetransformation may also be performed by a separate, dedicated device.Processor 412 may further transform the original and/or transformedsignals into any suitable domain. In an embodiment, step 704 is based atleast in part on a continuous wavelet transformation. For example, a PPGsignal may be transformed using a continuous wavelet transform asdescribed above with reference to FIG. 3( c). In an embodiment, step 704may include performing a continuous wavelet transform over thecomponents of a PPG signal, including an IR PPG signal component, a RedPPG signal component, or any combination of components.

Any number of computational and/or optimization techniques may beperformed in conjunction with the transformation of step 704. Forexample, if one or more scale bands associated with the physiologicalprocess of interest are approximately known or may be detected, thetransformation may initially be executed only over scales at or close tothese scale bands in order to reduce computation time. In an embodiment,if one or more scale bands communicate questionable or littleinformation about the physiological process of interest, thetransformation may not be executed over these scale bands. Any knowninformation about the scale bands of interest may be stored in memory(e.g., ROM 52 or RAM 54). Such known information may be keyed to thecharacteristics of the patient, which may be input via user inputs 56and used by monitor 14 to, for example, query a lookup table andretrieve the appropriate information. Additionally, any of thecalculations and computations described herein may be optimized for aparticular hardware implementation, which may involve implementing anyone or more of a pipelining protocol, a distributed algorithm, a memorymanagement algorithm, or any suitable optimization technique.

The transformation of the received signal at step 704 may also includepre- or post-processing transformations. These transformations mayinclude any one or more of the following: compressing, multiplexing,modulating, up-sampling, down-sampling, smoothing, taking a median orother statistic of the received signal, removing erroneous regions ofthe received signal, or any combination thereof. In an embodiment, anormalization step is performed which divides the magnitude of thereceived signal by a value, which may be based on at least one of themaximum of the received signal, the minimum of the received signal andthe mean of the received signal.

In an embodiment, at step 704, the signal may be filtered using anysuitable filtering method. In an embodiment, a signal received at sensor12 may be filtered by low pass filter 68 prior to undergoing additionalprocessing at microprocessor 48 within patient monitoring system 10. Thelow pass filter 68 may selectively remove frequencies that may later beignored by the transformation, which may advantageously reducecomputational time and memory requirements. In an embodiment, the signalreceived in step 702 may be high or band pass filtered to remove certainfrequencies. Such a filter may be a derivative filter. For example, aPPG signal may be filtered through a narrow band-pass filter that may becentered on the scale of a ridge of a scale band of interest. The PPGsignal may be filtered through any suitable additional number and typeof filters that may be centered on the scales of different ridges ofinterest. In an embodiment, the cutoff frequencies of a filter arechosen based on the frequency response of the hardware platformunderlying patient monitoring system 10.

Different transformations may be applied to any one or more of thecomponents of a signal, such as a Red PPG signal and an IR PPG signal.The transformation may be applied to a portion or portions of thereceived signal. The transformation of step 704 may be broken into oneor more stages performed by one or more devices within waveletprocessing system 400 (which may itself be a part of patient monitoringsystem 10). For example, a filtering operation may be applied by inputsignal generator 410 prior to passing the resulting signal 416 toprocessor 412, where it may undergo additional transformations.Embodiments of step 704 include any of the transformations describedherein performed in any suitable order.

At step 706, a scalogram may be generated based on the transformedsignal of step 704. Examples of such scalograms are depicted in FIGS. 3(a) and 3(b) and FIGS. 5( a) and 5(b). A scalogram may be generated byany of the techniques described herein, including those described abovewith reference to FIGS. 3( a) and 3(b). For example, processor 412 ormicroprocessor 48 may perform the calculations associated with thecontinuous wavelet transform of a signal and the derivation of thescalogram. As described above with reference to step 704, if one or morescale bands associated with the physiological process of interest areapproximately known or may be detected, the scalogram may be generatedonly over scales at or close to these scale bands in order to reducecomputation time. In an embodiment, if one or more scale bandscommunicate questionable or little information about the physiologicalprocess of interest, the scalogram may not be generated over these scalebands. In an embodiment, the scalogram generated in step 706 may bedisplayed for a user in any manner described herein, including viadisplays 20 and/or 28. The scalogram may also be recorded to a memorydevice (e.g., RAM 54 or a remote storage device) or a physical mediumsuch as a print-out. In an embodiment, the scalogram generated at step706 is based on any one or more features of the transformed signal ofstep 704. For example, the scalogram may represent the real part of atransformed signal, the imaginary part of a transformed signal, themodulus of a transformed signal, any other suitable feature of thetransformed signal, or any combination thereof.

At step 708, a first scale band may be identified within the scalogramgenerated at step 706. The first scale band identified at step 708 maycommunicate information regarding a physiological process of interest.In an embodiment, the first scale band may be a scale band associatedwith the pulse rate and may communicate information regarding apatient's blood oxygen saturation.

A scale band may be characterized as a region of a particular size andshape used to analyze selected features in the domain spacerepresentation of signal 416. The selected features of a scalogram maycommunicate information about physiological processes and help toidentify a first scale band. The selected features may be localized,repetitive, or continuous within one or more regions of the suitabledomain space representation of a signal such as signal 416. For example,the selected features may be localized, repetitive, or continuous inscale or time within a wavelet transform surface. A region's size andshape may be selected based at least in part on the physiologicalprocess of interest. As an illustrative example, in order to analyze apatient's pulse band for one or more selected features, the region maybe selected to have an upper and lower scale value in the time-scaledomain such that the region covers a portion of the band, the entireband, or the entire band plus additional portions of the time-scaledomain. The region may also have a selected time window width.

The bounds of the region may be selected based at least in part onexpected locations of the features. In an embodiment, the expectedlocations may be based at least in part on empirical data of a pluralityof patients. The region may also be selected based at least in part onpatient classification. For example, an adult's pulse band locationgenerally differs from the location of a neonatal patient's pulse band.Thus, the region selected for an adult may be different than the regionselected for a neonate. In some embodiments, a region may be selectedbased at least in part on features within a scalogram. For example, thescalogram may be analyzed to determine the location of a pulse band andits corresponding ridge. The pulse band ridge may be located usingstandard ridge detection techniques. In an embodiment, locating a ridgemay include identifying locations (a*,b*) in a scalogram which satisfythe relationship

$\begin{matrix}{{{{\frac{\partial}{\partial a}\left( \frac{{{T\left( {a,b,} \right)}}^{2}}{a} \right)}}_{{a = a^{*}},{b = b^{*}}} = 0},} & (23)\end{matrix}$

and locations in the vicinity of the ridge of Eq. 23. Such locations maybe orthogonal to the ridge of Eq. 23, and may have lower values of thequantity |T(a,b)|²/a. In an embodiment, locating a ridge may includeidentifying locations (a*,b*) in a scalogram which satisfy therelationship

$\begin{matrix}{{{{\frac{\partial}{\partial a}\left( {{T\left( {a,b,} \right)}}^{2} \right)}}_{{a = a^{*}},{b = b^{*}}} = 0},} & (24)\end{matrix}$

and locations in the vicinity of the ridge of Eq. 24. Such locations maybe orthogonal to the ridge of Eq. 24 and may have lower values of thequantity |T(a,b)|².

Ridges may also be detected using the techniques described in U.S.patent application Ser. No. 12/245,326, filed Oct. 3, 2008, entitled“SYSTEMS AND METHODS FOR RIDGE SELECTION IN SCALOGRAMS OF SIGNALS,”which is incorporated by reference herein in its entirety. As anillustrative example, if the ridge of a band were found to be atlocation X, the region may be selected to extend a predetermineddistance above and below location X. Alternatively, the band itself maybe analyzed to determine its size. The upper and lower bounds of theband may be determined using one or more predetermined or adaptivethreshold values. For example, the upper and lower bounds of the bandmay be determined to be the location where the band crosses below athreshold. The width of the region may be a predetermined amount of timeor it may vary based at least in part on the characteristics of theoriginal signal or the scalogram. For example, if noise is detected, thewidth of the region may be increased or portions of the region may beignored.

In some embodiments, the region may be determined based at least in parton the repetitive nature of the selected features. For example, a bandmay have a periodic feature. The period of the feature may be used todetermine bounds of the region in time and/or scale.

The region may be determined based at least in part on analysis of thephysiological signal in another domain. For example, a patient's pulserate may be determined by analyzing a PPG signal in the time domain. Theregion may be determined based at least in part on another receivedsignal. For example, a patient's pulse rate may be determined manuallyby a care provider, or by any other pulse rate detection technique ordevice. This information may then be transmitted electronically ormanually to a computational engine executing the calculations associatedwith step 708 and used to help localize the first scale band (e.g., viauser inputs 56 or any suitable input or interface to monitor 14). Step708 may involve the use of multiple sources of information from multiplepatient monitoring signals to identify the first scale band, and maycombine these sources of information in any suitable manner.

The size, shape, and location of the one or more regions may also beadaptively manipulated using signal analysis. The adaptation may bebased at least in part on changing characteristics of the signal orfeatures within the various domain spaces.

As a signal is being processed, for example by processor 412, the regionmay be moved over the signal in any suitable domain space over anysuitable parameter in order to determine the value or change in value ofthe selected features. The processing may be performed in real-time orvia a previously-recorded signal (stored, e.g., in RAM 54 or accessedfrom a remote device). For example, a region may move over the pulseband in the time-scale domain over time.

Once a first scale band is identified at step 708, at least one otherscale band may be identified at step 710. The at least one other scaleband may be related to the first scale band. The at least one otherscale band may be related to the first band, for example, because ofphysiological phenomena such as internal reflections. The at least oneother scale band may provide useful information regarding thephysiological process by identifying physiologically-relevant features,improving the quality of the derived information, or a combination ofthe two. As an example, information regarding a patient's blood oxygensaturation is contained in the pulse band as discussed above withreference to step 708. Information about blood oxygen saturation is alsocontained in scale bands of the scalogram that are associated withinteger multiples and near-integer multiples of the pulse rate. One willnote that such related scale bands in a continuous wavelettransformation of a signal are not simply representative of harmonics ofthe pulse rate appearing in the signal (as would be the case in aFourier transformation), but arise from correlated features within thesignal. Thus, these related scale bands may be associated with scalesthat are not integer multiples of a pulse rate.

In an embodiment, step 710 includes distinguishing between related scalebands that carry information about a physiological process and relatedscale bands that arise from artifact and are thus less useful indetermining physiological information. For example, when the first scaleband is a pulse band, the scale band associated with half the pulse ratemay not be included in the related scale bands identified in step 710 ifit is determined that such a band stems from patient motion or acondition unrelated to the physiological process of interest.

In an embodiment, related scale bands may be associated with scales thatare integer multiples of a first scale associated with the first scaleband. For example, a pulse band in a scalogram may be associated with ascale corresponding to the pulse rate, and related bands may beassociated with scales corresponding to twice the pulse rate scale,three times the pulse rate scale, etc. In some applications, relatedscale bands may be located by identifying other features within thescalogram, such as ridges or local maxima, which may occur atnon-integer multiples of a first scale band. Any number of related scalebands may be identified, including integer-multiple,non-integer-multiple, or a combination of the two.

Any number of other scale bands may be identified at step 710. Thenumber of scale bands identified may be fixed, or may be dynamic andbased at least in part on patient and/or environmental conditions. Forexample, a related scale band may only be identified if a particularfeature is present, such as an amplitude that exceeds a threshold. Theidentification of a scale band may be performed in conjunction with theassessment of scale band quality, as discussed further below withreference to step 712.

The location of the scale bands identified at steps 708 and 710 may beknown in advance, identified dynamically, identified at predeterminedintervals during a patient monitoring session, or any combinationthereof. For example, the location of scale bands known to be relevantto a particular physiological process may be stored within a memory suchas ROM 52 or RAM 54. In an embodiment, the location of the scale bandsmay be determined at regular intervals using any of the band locationand detection techniques described herein. In an embodiment, thelocation of the scale bands identified at steps 708 and 710 may bedetermined based at least in part on the physiological signal. Forexample, a change in patient condition may trigger a re-determination ofthe location of the scale bands.

At step 712, a band quality may be determined for one or more of thebands identified at steps 708 and 710. As discussed above with referenceto FIGS. 5( a) and 5(b), the amount of useful information about aphysiological process of interest may vary between the identified scalebands. Certain types of noise and artifact may influence certain scalebands more than others, and such noise may reduce the amount of usefulinformation that can be obtained from the band. For example, patientmovement may distort the scale bands associated with lower scales, whilecertain types of hardware noise may distort the scale bands associatedwith higher scales. Assessing the quality of a scale band may involvedetermining an amount (relative or absolute) of useful information aboutthe physiological process of interest contained in the scale band.Assessing the quality of a scale band may involve determining an amountof noise affecting the scale band.

Determining the quality of a scale band, as performed at step 712, mayinvolve deriving a band quality metric. A band quality metric mayprovide a qualitative or quantitative measurement of the quality of theinformation contained in the scale band. The band quality metric may bea single value, or may be a waveform that varies in time, scale, orboth. In an embodiment, step 712 involves using any one or more of thefollowing metrics, in any combination, to determine the quality of ascale band.

1. Metrics based on scalogram energy. For example, a measure of thequality of a scale band may involve taking the ratio of the total energyof the scale band (or a portion of the scale band) and the total energyin a localized region of the scalogram (which may include the entirescalogram). In an embodiment, the energy in a region of the scalogramwith boundary W may be calculated in accordance with

$\begin{matrix}{\underset{W}{\int\int}\frac{{{T\left( {a,b} \right)}}^{2}}{a^{2}}{a}{{b}.}} & (25)\end{matrix}$

This metric, and related metrics, may be analogous to a signal-to-noise(SNR) ratio, and any such commonly-used SNR metrics may be employed atstep 712.2. Metrics based on uniformity of scalogram features. These metrics maymeasure the consistency of features within each scale band, with greaterconsistency suggesting an absence of noise or corrupting interference.For example, the quality of a scale band may vary inversely orcomplementarily to the standard deviation of the amplitude of thescalogram within the scale band. The quality of a scale band may varyinversely or complementarily to any variability metric, including thosebased on the time derivatives of a portion or portions of the scalogram.3. Metrics based on relative comparisons between scale bands. In anembodiment, the quality of a scale band is determined based at least inpart on at least one other scale band. For example, the quality of agiven scale band may be based at least in part on a comparison betweenan energy within the given scale band and a total energy across allidentified scale bands. Such a comparison may take the form of a ratio,for example.4. Metrics based on correlation between corresponding scale bands ofreceived signals with multiple components. As discussed above, areceived signal may include multiple components. For example, differentcomponents of the received signal may correspond to differentfrequencies, such as a Red PPG signal and an IR PPG signal. Thesecomponents may each undergo steps similar to steps 704-710. These stepsmay be performed independently for each of the components, or may bedependent. At steps 708 and 710, scale bands may be identified inscalograms associated with each of the components. These scale bands maycorrespond to similar regions in the scalogram (e.g., scale ranges andtemporal ranges), and thus may be compared between components. In oneembodiment, a scale band associated with one component is correlatedwith a scale band associated with another component. In an embodiment, aquality metric may be based on the Pearson product moment correlation,and may be calculated in accordance with

$\begin{matrix}{{\frac{1}{T - 1}{\sum\limits_{i = 1}^{T}\; {\left( \frac{x_{i} - \overset{\_}{x}}{s_{x}} \right)\left( \frac{y_{i} - \overset{\_}{y}}{s_{y}} \right)}}},} & (26)\end{matrix}$

where T is the number of samples or measurements; x_(i) and y_(i) arethe ith measurements of signals x and y, respectively; x and y are therespective sample means; and s_(x) and s_(y) are the respective samplestandard deviations. A correlation may be calculated in accordance withany known techniques, including those described in U.S. patentapplication Ser. No. 12/398,826, filed Mar. 5, 2009, entitled “SYSTEMSAND METHODS FOR MONITORING HEART RATE AND BLOOD PRESSURE CORRELATION,”which is incorporated by reference herein in its entirety.5. Metrics based on noise estimates. In an embodiment, determining aband quality may include assessing an amount of noise present in theband. Assessing an amount of noise may involve detecting acharacteristic scalogram feature, such as a feature corresponding to thenoise signature of a hardware device in the environment. Assessing anamount of noise may involve detecting an abnormality in features of thescalogram, such as those that arise in a PPG scalogram during patientmovement. The amount of noise may be assessed by a quantitative orqualitative assessment, which may be used in an inverse or complementaryrelationship to a band quality assessment. In an embodiment, noise maybe characterized by determining an energy in a region between two ormore bands. This energy may itself serve as a noise measure, or may beused to determine a ratio of band energies that may serve as a noisemeasure. For example, noise may be measured by calculating a ratio ofthe energy of a region between two bands to the energy within one orboth of the two bands. Noise may also be characterized by assessing thedistribution of energy within one or more regions of a scalogram. Forexample, noise may be characterized by assessing the intermittencyand/or entropy of an energy signal along a band or ridge. In anembodiment, noise may be characterized by comparing characteristics of aband or ridge with a theoretical model of the band or ridge. Thetheoretical model may represent a band or ridge in the absence of noise,and thus provide a point of comparison for a band or ridge based onactual patient data. Additional noise characterization techniques aredescribed in a co-pending U.S. patent application of Addison et al.,entitled “SYSTEMS AND METHODS FOR EVALUATING A PHYSIOLOGICAL CONDITION,”(Attorney Docket No. H-RM-01271 (COV-35)), which is incorporated byreference herein in its entirety.

At step 714, at least two of the bands identified in steps 708 and 710may be combined. For example, the bands may be summed together. In anembodiment, the scalogram data included in each of the bands isconcatenated across all bands to form a combined data set.

The combination of the identified bands may be based at least in part onthe band quality or qualities determined at step 712. In an embodiment,the selection of points from each scale band to be included in acombined data set is made based on the band quality determined at step712. In such an embodiment, more points may be included from scale bandsof higher quality than from scale bands of lower quality. In anembodiment, the bands may be combined by performing a weightedsummation, where the weighting of a particular scale band depends atleast in part on the band quality. For example, a combined signal,x_(total), may be calculated in accordance with

$\begin{matrix}{{x_{total} = {\sum\limits_{i = 1}^{N}\; {w_{i}x_{i}}}},} & (27)\end{matrix}$

where N represents the total number of identified bands, w_(i)represents the weight associated with band i and x_(i) represents thescalogram values of band i. The weight w_(i) may be calculated in any ofa number of ways. In an embodiment, the weight w_(i) is based on thefraction contained in band i of the total energy contained in allidentified bands as described above. In an embodiment, the weight w_(i)is a monotonic transformation of any of the band quality metricsdescribed above with reference to step 712. The bands may also becombined via any suitable nonlinear combination, which may or may notinclude weights as described above.

In an embodiment, the combining of scale bands at step 714 may beperformed for each component of a received signal with multiplecomponents. For example, step 714 may result in a combined Red PPG bandand a combined IR PPG band when the received signal has componentsincluding a Red PPG signal and an IR PPG signal.

In an embodiment, combining the scale bands at step 712 may include athreshold test on one or more of the band qualities. The threshold testmay determine the degree to which a scale band should be included in acombination. Generally, a threshold test on a value may test any of anumber of threshold conditions, including whether the value exceeds asingle threshold, whether the value is below a single threshold, orwhether the value falls within a specified range or ranges. Thethreshold test may be fixed, and retrieved by processor 412 from ROM 52or RAM 54. The threshold test may be dynamic and depend, for example, onpreviously calculated scalograms, previously calculated band qualities,band qualities of more than one band, or any combination thereof. Thethreshold test may also depend on secondary signal quality indicators,such as an electromagnetic noise measuring device or a signal arisingfrom sensor 418 indicating a malfunction or undesirable operatingcondition. In an embodiment, a band may be included in the combinationif its associated band quality exceeds a threshold, and may not beincluded otherwise. In an embodiment, a band may be included in thecombination with a first weight if an associated band quality exceeds afirst threshold, and may be included in the combination with a second,higher weight if the associated band quality exceeds a second, higherthreshold. These specific embodiments are illustrative, and appropriatethreshold tests may include any number of threshold conditions andresulting implications for the band combination calculation.

In an embodiment of process 700, an analysis of one or more timewaveforms may also be performed. Examples of such time waveforms aredepicted in FIG. 6. In such an embodiment, received signals ortransformations of received signals may be analyzed in the time domainto assess quality and/or noise. The signals examined in the time domainmay be separately based on each of the bands identified in steps 708 and710, or may be based on a combination of the bands (such as thecombination resulting from step 714). As discussed above with referenceto FIG. 6, an analysis of a time waveform may calculate a noise metricfor one or more subwindows of a time window. The duration of a subwindowmay be fixed, or may vary dynamically. In an embodiment, the duration ofa subwindow of a time waveform containing information about a patient'spulse rate may depend upon the pulse rate. For example, each subwindowmay corresponds to a single pulse, with durations that vary betweensubwindows to match the varying time between pulses. In an embodiment,subwindows of a time waveform may be removed from the data set orre-weighted based on a quality and/or noise assessment. For example,this assessment may be used in a threshold test as described above withreference to step 714. In an embodiment, at a particular time, the Mbest subwindows over a previous time interval will be retained forfurther analysis and the others discarded, based on a quality and/ornoise assessment. In an embodiment, a quality and/or noise assessmentmay involve using analogs of any of the metrics described above withreference to determining scale band quality at step 712, including thefollowing:

1. Metrics based on an energy of a time waveform. For example, a measureof the quality of a subwindow may involve taking the ratio of the totalenergy of the subwindow and the total energy in a portion of the window(which may include the entire window). This metric, and related metrics,may be analogous to a signal-to-noise (SNR) ratio, and any suchcommonly-used SNR metrics may be employed in an analysis of a timewaveform.2. Metrics based on uniformity of waveform features across subwindows.3. Metrics based on relative comparisons between subwindows.4. Metrics based on correlation between corresponding waveforms ofreceived signals with multiple components.5. Metrics based on noise estimates.6. Metrics based on determined physiological information. In anembodiment, subwindows of a time waveform may be removed from the dataset or re-weighted after an initial determination of physiologicalinformation from the combined scale bands. The determination ofphysiological information is discussed below with reference to step 716.

The analysis of a time waveform as described herein may be performedfollowing any appropriate step of process 700 (e.g., after step 714 oriteratively with step 716) or may not be performed at all.

At step 716, physiological information may be determined based on thecombining of scale bands at step 714. As described above, features ofthe scalogram may be representative of a variety of physiologicalprocesses. The physiological information determined at step 716 may bequantitative or qualitative, and may be the result of applying apredictive model such as a neural network to the combined signal(discussed in additional detail below). For example, the physiologicalinformation may be at least one of an identification of a medicalcondition of the patient and a current physiological measurement.

In an embodiment, the signal received at step 702 may be a signal withmultiple components, such as a Red PPG signal and an IR PPG signal, andthe first band identified at step 708 may be a pulse band. In such anembodiment, the combining step 714 may result in a Red combined band andan IR combined band, and the combined bands may be used to compute ablood oxygen saturation using any number of techniques. Severaltechniques that may be used to compute a blood oxygen saturation from aRed combined band and an IR combined band are described, for example, inU.S. patent application Ser. No. 10/547,430, filed Feb. 27, 2004,entitled “METHOD OF ANALYZING AND PROCESSING SIGNALS,” which isincorporated by reference herein in its entirety.

At step 718, the physiological information determined at step 716 may beoutput. Physiological information may be output through a graphicalrepresentation, a quantitative representation, a qualitativerepresentation, or combination of representations, via output 414 andmay be controlled by processor 412. Output 414 may transmitphysiological information by any means and through any format useful forinforming a patient and a care provider of a patient status and mayinvolve recording the physiological information to a storage medium.Quantitative or qualitative physiological information provided by output414 may be displayed on a display, for example, on display 28. Agraphical representation may be displayed in one, two, or moredimensions and may be fixed or change with time. A graphicalrepresentation may be further enhanced by changes in color, pattern, orany other visual representation. Output 414 may communicate thephysiological information by performing at least one of the following:presenting a screen on a display; presenting a message on a display;producing a tone or sound; changing a color of a display or a lightsource; producing a vibration; and sending an electronic message. Output414 may perform any of these actions in a device close to the patient,or at a mobile or remote monitoring device as described previously. Inan embodiment, output 414 produces a continuous tone or beeping whosefrequency changes in response to changes in a physiological process ofinterest. In an embodiment, output 414 produces a colored or flashinglight which changes in response to changes in a physiological process ofinterest.

After or during the output of physiological information at step 718, theprocess 700 may begin again. Either a new signal may be received, or thephysiological information determination may continue on another portionof the received signal(s). In an embodiment, processor 512 maycontinuously or periodically perform steps 702-718 and update thephysiological information. The process may repeat indefinitely, untilthere is a command to stop the monitoring and/or until some detectedevent occurs that is designated to halt the monitoring process. Forexample, it may be desirable to halt a monitoring process when adetected noise has become too great, or when a patient has undergone achange in condition that can no longer be sufficiently well-monitored ina current configuration. In an embodiment, processor 412 performsprocess 700 at a prompt from a care provider via user inputs 56. In anembodiment, processor 412 performs process 700 at intervals that changeaccording to patient status. For example, process 700 will be performedmore often when a patient is undergoing rapid changes in physiologicalcondition, and will be performed less often as the patient's conditionstabilizes.

Several of the steps of process 700 may be aided by the use of apredictive model. For example, a predictive model may be employed in atleast one of step 708 for identifying a first scale band, step 710 foridentifying at least one related scale band, step 712 for determining aband quality, and step 714 for determining physiological information. Inan embodiment, a predictive computational model may detect andcharacterize a noise or interference source affecting the receivedsignal. In an embodiment, a predictive computational model determinesestimates of a patient's current physiological status and prognosis aspart of the determined physiological information. A predictivecomputational model executed, for example, by processor 412, may bebased in part on at least one of the following data sources: thereceived signal (e.g., input signal 416); additional physiologicalsignals; patient characteristics; historical data of the patient orother patients; and computational or statistical models of physiologicalprocesses. Processor 412 may retrieve any of these data sources frommemory such as ROM 52 or RAM 54, from an external memory device, or froma remote device. The structure of a predictive computational model may,for example, be based on any of the following models: a neural network,a Bayesian classifier, and a clustering algorithm. In an embodiment,processor 412 develops a predictive neural network for noise assessmentbased at least in part on historical data from the given patient and/orother patients. In some embodiments, processor 412 implements thepredictive computational model as a hypothesis test. Processor 412 maycontinually refine or augment the predictive computational model as newpatient data and/or physiological signals are received. The predictivemodel may also be refined based on feedback from the patient or careprovider received through the user inputs 56. Other predictiveframeworks may include rule-based systems and adaptive rule-basedsystems such as propositional logic, predicate calculus, modal logic,non-monotonic logic and fuzzy logic.

It will also be understood that the above method may be implementedusing any human-readable or machine-readable instructions on anysuitable system or apparatus, such as those described herein.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made by those skilled in theart without departing from the scope and spirit of the disclosure. Thefollowing claims may also describe various aspects of this disclosure.

What is claimed is:
 1. A method for processing a physiological signal ofa subject, comprising: receiving, from a sensor, the physiologicalsignal; determining, using a processor, a pulse rate of the subjectbased at least in part on the physiological signal; transforming, usingthe processor, the physiological signal into a transformed signal basedat least in part on a wavelet transform; determining, using theprocessor, a quality associated with each of a plurality of scales ofthe transformed signal, wherein each of the plurality of scales is basedat least in part on the pulse rate; combining, using the processor, theplurality of scales of the transformed signal to generate combined scaledata based at least in part on the associated qualities; anddetermining, using the processor, physiological information of thesubject based at least in part on the combined scale data.
 2. The methodof claim 1, wherein the plurality of scales corresponds to scalesassociated with the pulse rate and multiples of the pulse rate.
 3. Themethod of claim 1, wherein the plurality of scales comprises a firstscale corresponding to the pulse rate, a second scale corresponding totwo times the pulse, and a third scale corresponding to three times thepulse rate.
 4. The method of claim 1, wherein combining the plurality ofscales comprises calculating a weighted combination based on each of theplurality of scales, wherein the weight associated with each of theplurality of scales is based at least in part on a correspondingassociated quality.
 5. The method of claim 1, further comprisingremoving a portion of a scale over a time interval based at least inpart on a quality over the time interval.
 6. The method of claim 1,further comprising: receiving, from the sensor, a second physiologicalsignal; and transforming, using the processor, the second physiologicalsignal into a second transformed signal based at least in part on awavelet transform, wherein determining the quality associated with eachof the plurality of scales is based on the transformed signal and thesecond transformed signal.
 7. The method of claim 1, wherein determiningthe quality associated with each of a plurality of scales comprisesmeasuring the consistency of features in the transformed signal.
 8. Themethod of claim 1, wherein determining the quality associated with eachof a plurality of scales comprises comparing energy of a scale andenergy of the plurality of scales.
 9. The method of claim 1, whereindetermining the quality associated with each of a plurality of scalescomprises assessing noise in the transformed signal.
 10. The method ofclaim 1, wherein determining the physiological information of thesubject comprises determining blood oxygen saturation of the subject.